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Population change map

Source: vignettes/Interactive-map.Rmd
Interactive-map.Rmd

Use the interactive map below to check population changes. Zoom in to see where Podocnemis unilfilis is Endangered based on IUCN Red List criteria - A3bd.

This is an extension of Norris et. al. 2019 that includes:

  • Stochastic population projections.
  • Future impacts to populations caused by human acessibility (hunting and habitat change) and actions that reduce river connectivity.

Map

Due to the number of points, the map can become slow to respond when you zoom in. Zoom out to a level showing fewer points and you can pan around the map to find the area of interest. Then zoom in again to check the coloured points.

When you zoom in you will see shaded points. The points follow rivers produced by remote sensing (Free-Flowing Rivers, 2019: https://doi.org/10.1038/s41586-019-1111-9) The points are brown where populations are predicted to decline by 50% or more within 3 generations (42 years). Brown points therefore represent rivers where the species is Endangered, following the IUCN Red List population size reduction criteria - A3bd (https://www.iucnredlist.org/about/faqs).

Summaries

A summary of the results is presented below. This includes summaries by country and major basin.

Summary by country.

Country Population change Endangered (prop) river length (km) length End. length not End.
Bolivia -0.576 0.70 22217 15516 6701
Brazil -0.626 0.74 190555 141183 49372
Colombia -0.483 0.63 38552 24122 14430
Ecuador -0.781 0.85 8302 7066 1236
French Guiana -0.480 0.63 3298 2082 1216
Guyana -0.249 0.45 7372 3311 4061
Peru -0.684 0.78 47363 36750 10613
Suriname -0.261 0.46 5756 2643 3113
Venezuela -0.383 0.56 30022 16714 13308
Total 353437 249387 104050

Summary by basin.

Using results from updated Norris et. al. 2019.

Basin Population change Endangered (prop) river length (km) length End. length not End.
Amazon -0.673 0.77 170905 131958 38947
Coastal North -0.549 0.68 12700 8626 4074
Coastal South -0.976 1.00 5711 5711 0
Orinoco -0.588 0.70 26533 18677 7856

Same using Free-flowing rivers data.

Basin population change Endangered (prop) river length (km) length End. length not End.
Amazon -0.606 0.72 280121 202508 77613
Coastal North -0.339 0.52 20835 10879 9956
Coastal South -0.973 1.00 6467 6467 0
Orinoco -0.501 0.64 46014 29533 16481

Summary by major basins and countries.

Basin Country Population change Endangered (prop) river length (km) length End. length not End.
Amazon Bolivia -0.859 0.91 18739 17098 1641
Amazon Brazil -0.627 0.74 116106 85750 30356
Amazon Colombia -0.376 0.54 7769 4229 3540
Amazon Ecuador -0.917 0.95 3330 3176 154
Amazon Guyana -0.973 1.00 299 299 0
Amazon Peru -0.861 0.91 23447 21304 2143
Amazon Venezuela 0.240 0.08 1215 102 1113
Coastal North Brazil -0.643 0.76 1681 1279 402
Coastal North French Guiana -0.644 0.76 1833 1390 443
Coastal North Guyana -0.555 0.68 4376 2973 1403
Coastal North Suriname -0.367 0.54 3908 2103 1805
Coastal North Venezuela -0.931 0.98 902 881 21
Coastal South Brazil -0.976 1.00 5711 5711 0
Orinoco Colombia -0.564 0.69 10268 7037 3231
Orinoco Venezuela -0.603 0.72 16265 11640 4625

Same with Free-flowing rivers…..plot results

# Make summaries
tab_sum <- points_bau_ffr |> 
  dplyr::mutate(flag_EN = if_else(fem_diff_t42 <= -0.5, 1, 0)) |>
  dplyr::group_by(COUNTRY, BASIN_NAME) |> 
  dplyr::summarise(pop_start = sum(fem_t0), 
                   pop_end = sum(fem_t42), 
                   pop_change = round(((sum(fem_t42) - sum(fem_t0)) / sum(fem_t0)), 3),
                   diff_mean = mean(fem_diff_t42), 
                   change_lcl_95 = Hmisc::smean.cl.boot(fem_diff_t42)["Lower"], 
                   change_ucl_95 = Hmisc::smean.cl.boot(fem_diff_t42)["Upper"],
                   diff_median = median(fem_diff_t42),
                   diff_q25 = quantile(fem_diff_t42, probs = 0.25), 
                   diff_q75 = quantile(fem_diff_t42, probs = 0.75),
                   length_river = n(), 
                   length_endangered = sum((flag_EN))) |>
  dplyr::ungroup() |> 
  dplyr::mutate(proportion_endangered = round((length_endangered / length_river), 2), 
                length_label = round((length_river / 1000), 1)
                ) |> 
  dplyr::mutate(threat_status = case_when(diff_mean <= -0.8 ~ "Critically Endangered", 
                                   diff_mean <= -0.5 ~ "Endangered", 
                                   diff_mean <= -0.3 ~ "Vulnerable", 
                                   diff_mean <= -0.2 ~ "Near Threatened")) 
#> `summarise()` has grouped output by 'COUNTRY'. You can override using the
#> `.groups` argument.
tab_sum$bc <- factor(paste(tab_sum$BASIN_NAME, tab_sum$COUNTRY, sep = "\n"))
# levels(tab_sum$bc)

Table showing data values used in graph.

Basin Country population change Endangered (prop) river length (km)
Amazon Bolivia -0.576 0.70 22217
Amazon Brazil -0.616 0.73 180434
Amazon Colombia -0.352 0.53 18687
Amazon Ecuador -0.781 0.85 8302
Amazon Guyana -0.982 1.00 423
Amazon Peru -0.684 0.78 47363
Amazon Venezuela 0.069 0.22 2695
Coastal North Brazil -0.516 0.66 3653
Coastal North French Guiana -0.480 0.63 3297
Coastal North Guyana -0.202 0.41 6928
Coastal North Suriname -0.261 0.46 5756
Coastal North Venezuela -0.590 0.72 1201
Coastal South Brazil -0.973 1.00 6466
Orinoco Colombia -0.606 0.72 19865
Orinoco Venezuela -0.420 0.58 26126

Next steps